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1 Next presentation starting soon Business Analytics using Big Data to gain competitive advantage

2 If every image made and every word written from the earliest stirring of civilization to the year 2003 were converted to digital information, the total would come to five exabytes. An exabyte is one quintillion bytes, or one billion gigabytes or just think of it as the number one followed by 18 zeros. That's a lot of digital data, but it's nothing compared with what happened from 2003 through 2010: We created five exabytes of digital information every two days. Get ready for what's coming: By next year, we'll be producing five exabytes every 10 minutes. How much information is that? The total for 2010 of 912 exabytes is the equivalent of 18 times the amount of information contained in all the books ever written. The world is not just changing, and the change is not just accelerating; the rate of the acceleration of change is itself accelerating WSJ 2/22/2012 book review Abundance: The Future Is Better Than You Think" PwC 2

3 Big Data* is a term applied to data sets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within tolerable elapsed time. Characteristics of Big Data are: Volume: Big Data sizes are a constantly moving target ranging from a few dozen terabytes to many petabytes of data in a single data set Velocity: Speed of generation, web logs, sensor data, complex event processing Variety: structured, un-structured and semi-structured formats, graphs, GIS, and others Big Data technologies have been popularized by Google, Yahoo, Facebook and others as they extract value out of the tremendous volume of data they handle on a day to day basis. PwC 3

4 Data Challenges The information is growing at a phenomenal rate.. Information growing at exponential rate New sources of information are constantly emerging The amount of information collected is growing exponentially Driving value from information is a competitive opportunity Required to stay ahead of the competition - with the right products in the right channels at the right price Understanding customer behavior and real time market conditions Increasingly not doing this will be a competitive disadvantage PwC Challenges of information are daunting If you build it, they will not come more likely they will run Information overload is real danger; manage velocity, access, validity, linkage and relevance Having KPIs doesn t mean you make good decisions 4

5 Big Data - Distributed Computing Architecture Figure 1A High Processing power Enterprises facing scaling and capacity/cost problems Google, Amazon, Facebook, Twitter, etc. (all use nonrelational data stores for reasons to scale) Low Processing power Most Enterprises Cloud users with low compute requirements Centralized Compute architecture Distributed Compute architecture A shared-nothing architecture requires an efficient parallel-processing application service such as Parallel DMBS or the MR PwC

6 Big Data Technologies driving Analytics Columnar Storage and Databases In Memory Databases and Analytics Convergence of Machine Learning & Data Mining with Big Data Support of MR by major RDBMS vendors Management of structured and un-structured content Advances in Hadoop Hive and HBASE Integration RHadoop Project (R and MR Integration) PwC 6

7 Big Data BI The convergence of business intelligence and search technologies enables companies to expand the BI landscape to a wide variety of business users and business processes combining the simplicity of search and power of BI. Unstructured Data (HDFS) Structured Data (RDBMS) Oracle Big Data Hadoop/ Mapreduce ETL/ELT CDC RealTime Oracle Enterprise Applications Business Intelligence (OBIEE & Endeca) DM(1) DM(3) DM(5) DM(2) DM(4) DM(n) Oracle Exadata Keyword- and text-based simple searching Searching structured and unstructured data Faster and easier for business users Automated relevancy and linkages to BI services (dashboards, reports, and data services) Linkage between structured and unstructured data Ability to understand and answer questions with simple English queries PwC 7

8 Big Data Analytics : Social Media Call Center SMS Click Stream, Website Search (Paid and Organic) s and Suggestions Mobile Location Data Ratings and Reviews Transactions Surveys External Vendor Data Loyalty Market Research Demographics Products Third Party Segmentation Blogs, Twitter Campaigns Unstructured Data Warehouse Match Log Identify Structured Data Warehouse Consumer Insights Analysis PwC 8 Extract Golden Nuggets of Information Predictive Analytics- Model Conception, Testing and Promotion Analytics Master

9 Big Data Analytics by Industry 1. Health Industries Consumerism Patient Support Models and Wellness Plans Social Strategies and Mobile Healthcare 2. Retail Consumer behaviors and Customer experience Social Strategies and Mobile Strategies Beyond Price Point Themes 4. Financial Services Mobile Banking and Digital Wallets Regulatory Challenges Risk Sensitivity 5. Utilities Smart Grids and Smart Meters Customer Satisfaction and Incentive Models Asset Management and Aging workforce 3. Hospitality Consumer behaviors and Customer experience Social Strategies and Mobile Strategies Market segmentation and pricing analytics 6. Entertainment & Media Digital Rights Management User Generated Content Piracy PwC 9

10 Thank you 2011 PricewaterhouseCoopers LLP. All rights reserved. In this document, PwC refers to PricewaterhouseCoopers LLP, which is a member firm of PricewaterhouseCoopers International Limited, each member firm of which is a separate legal entity. This document is for general information purposes only, and should not be used as a substitute for consultation with professional advisors

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